Akhil Lodha is the CEO and Founder of StratiFi Technologies, a company that provides an AI-powered platform to streamline investment workflows for wealth managers. With a strong background in quantitative finance, Akhil has over 12 years of experience in managing investment risk, having previously worked at Citigroup and led a $400M asset management firm. His academic credentials include a degree from the Indian Institute of Technology (IIT Bombay) and Carnegie Mellon University (CMU). Akhil’s vision with StratiFi is to unlock financial freedom by helping people secure their future.

Akhil Lodha

Acquisition-driven RIAs rarely struggle with deal flow.

They struggle with integration.

If onboarding a new firm takes days of manual reconciliation, growth eventually stalls.

Creekmur saved 50–200 hours per acquisition transition once risk alignment became systematic instead of manual.

M&A doesn’t break firms. Fragmented infrastructure does.

CEO at StratiFi - AI & Compliance for Wealth Management

M&A is a growth strategy — until onboarding becomes the bottleneck.

Creekmur Wealth Advisors nearly doubled acquisitions and advisors in one year. Their existing risk tools couldn’t keep up.

Before StratiFi:
- Poor interoperability across systems
- Manual portfolio risk reconciliation
- Time-intensive acquisition transitions
- Limited capacity to absorb new firms 
- Hours spent organizing data instead of advising clients

After centralizing risk and compliance with StratiFi, Creekmur now:
✅ Manages 6K accounts and $1B in assets from one dashboard
✅ Supervises 14+ advisors with ongoing risk alignment
✅ Completes client risk assessments in minutes
✅ Saves 50–200 hours per acquisition transition

Risk alignment is no longer a manual event. It’s an embedded workflow.

When acquisition velocity increases, infrastructure has to keep pace.

Read more at the link in the comments 👇

At scale, supervision has to be system-driven, not spreadsheet-driven.

Kingsview hit that inflection point at $8B AUM, 100+ advisors, and 30K+ accounts. With StratiFi Technologies Inc, they moved from manual drift reviews and paper RTQs to real-time oversight and a single dashboard.

Sharing the results below, plus the full case study.

At $8B AUM and 100+ advisors, spreadsheets stop being “good enough.”

Before StratiFi, Kingsview Partners relied on manual drift reviews and paper RTQs to supervise 30K+ accounts.

After implementing StratiFi:
✅ Identify portfolio drift 4x faster
✅ Supervise 100+ advisors from one dashboard
✅ Monitor 30K+ accounts in real time
✅ Eliminate compliance bottlenecks

When growth accelerates, manual oversight breaks.

Full case study at the link in our comments.

The SEC released its 2026 examination priorities on November 17, 2025.

For investment advisers, the focus is unmistakable: fiduciary duty, conflicts of interest, and whether your compliance program actually works.

Not whether it exists. Whether it works.

Here is what examiners will be testing:

→ Whether your investment advice aligns with clients' objectives, risk tolerances, and liquidity needs

→ Whether your firm is identifying and disclosing conflicts of interest that affect the impartiality of your advice

→ Whether trading activity demonstrates best execution

→ Whether your compliance policies are implemented, enforced, and monitored on an ongoing basis

The SEC is also examining how firms use and represent AI capabilities. But the core exam focus has not changed. It is still about fiduciary duty, suitability, and active supervision.

And this is where I see the biggest gap across our industry.

Many compliance teams today are reviewing accounts manually, pulling data from multiple systems, and running periodic reviews that leave gaps between each check. When an examiner asks, "How were you supervising this account between your last two quarterly reviews?", too many firms do not have a good answer.

This is the problem we built StratiFi to solve.

ComplianceIQ continuously monitors every account for Reg BI violations, mutual fund share class issues, cash and position concentration breaches, suitability gaps, and trading activity anomalies. Each flag comes with specific violation details, recommended actions, and the documentation a compliance team needs for regulatory review.

PRISM, our risk analysis engine, scores portfolio risk on a 1-10 scale using institutional-grade methodology, including Value-at-Risk metrics, concentration analysis, and factor exposures. When a portfolio drifts from a client's investment policy or risk tolerance, the system flags it in real time.

The SEC is telling firms that compliance programs need to demonstrate active, ongoing supervision. Not quarterly snapshots. Continuous oversight.

That is what the regulatory environment demands. And that is what we built.

What is the biggest compliance monitoring challenge your firm is facing heading into exam season?

#SECExam #ComplianceMonitoring #RIACompliance #WealthManagement #RegBI #ComplianceTech #RegTech

TELUS

Spearheaded the development of a real-time fraud detection system using LightGBM, resulting in millions in prevented losses across retail channels in 2024

Senior Data Scientist

California Department of Fish and Wildlife

•	Trained and deployed a tweet classification model to identify sentiment of relevant tweets relating to human-wildlife interaction in the state of California.
•	Designed a dashboard using Plotly/Dash to display real time streaming Twitter information (~300 tweets per day with selected keywords)
•	Implemented a pipeline using Tweepy and pandas to stream real time tweets and hosted it in an AWS EC2 instance

Data Science Intern

HSBC

•	Managed a team of four analysts as part of the portfolio management team 
•	Developed quantitative margining (using pandas, numpy, sklearn) and risk management framework, by employing time series analysis and portfolio simulation for trading strategies.

Risk Management

Deutsche Bank

•	Risk management and trading of dividend and equity repo in the US and Canadian equity markets. 
•	Developed Python based applications to forecast dividend curves for arbitrage opportunities aggregating multiple data feeds (Bloomberg, MarkIT, DB analyst predictions and my own fundamental analysis)

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